Li Guitao’s article “What Should ‘Artificial Intelligence’ Be Called?” is not merely a discussion of concepts; it deeply deconstructs the essence of artificial intelligence from the perspectives of philosophy, science, anthropology, and linguistics. It proposes a foundational logic and contemporary proposition for naming artificial intelligence, transcending the technical realm to address the fundamental relationship between humanity and artificial intelligence, combining profound thought with practical significance. Below, we interpret its core value and historical significance from four dimensions:

1. Philosophical Depth: Establishing a “Meta-Concept” Framework for Artificial Intelligence

The article’s core philosophical contribution is elevating artificial intelligence from a technical tool to a meta-concept of the new era, constructing a philosophical system of “one premise and three principles” that achieves an ontological positioning and epistemological construction of artificial intelligence, essentially legislating artificial intelligence philosophically.

Essence of the Meta-Concept:

It clarifies that artificial intelligence is not merely a technical concept but a meta-concept on par with “heaven, earth, and humanity”—as the foundation for constructing the world of artificial intelligence, it cannot be defined by other concepts but can give rise to all secondary concepts within the intelligent system. This definition breaks the narrow Western view of artificial intelligence as merely “artificial intelligence,” endowing it with ontological significance.

The Philosophical Loop of the Three Principles:

1. Humanity: Anchoring the core that “humans are the meta-concept of artificial intelligence,” it clarifies that artificial intelligence is a “derivative, opposite, and externalization” of human essence, defined entirely by humans. This negates the extreme view that “artificial intelligence surpasses humanity” and rejects the shallow perception of it as a mere material tool, establishing the immovable philosophical premise of human subjectivity.

2. Self-Referentiality: Revealing the self-referential characteristics of artificial intelligence (such as “thinking about its own thinking”), it explains the possibility of consciousness emergence and points out the logical paradoxes that can arise from improper naming, allowing artificial intelligence to reflect on the essence of human intelligence.

3. Generativity: Proposing that artificial intelligence is a “dynamically generated process rather than a static entity,” its essence emerges through interaction with humans. This view integrates the phenomenological idea that “relation precedes entity,” breaking static definitions of artificial intelligence and leaving philosophical space for its future evolution.

Ultimate Inquiry into the Nature of Intelligence:

It points out that artificial intelligence is the only discipline that studies “intelligence itself” as its object, and the process of “constructing intelligence” is the most thorough philosophical questioning of human intelligence—ranging from imitation reasoning to generative creation, the history of artificial intelligence development is essentially humanity’s continuous repositioning and reflection on “what is intelligence” and “what it means to be human.” This thought elevates the study of artificial intelligence to a philosophical proposition of human self-awareness.

2. Scientific Value: Revealing the Scientific Laws of Naming to Avoid Systemic Risks in AI Development

The article transcends the technology itself, providing scientific guidance for the development of artificial intelligence from the perspective of conceptual system construction, with value reflected in two aspects: “lessons learned” and “establishing scientific principles,” filling the gap of “concept standardization” in the field of artificial intelligence.

Unveiling the Scientific Disaster Chain of Improper Naming:

Through numerous cases from programming, biomedicine, and information technology, it outlines a vicious cycle of “naming chaos → cognitive overload → communication cost surge → technical debt → innovation barriers → catastrophic accidents,” clearly indicating that the chaos of the conceptual system is a more fundamental systemic risk than technical vulnerabilities (e.g., the naming error of Changchun New Alkali leading to a medical disaster, the terminology confusion in distributed computing causing technical debt). This finding serves as a warning for conceptual construction in the field of artificial intelligence.

Establishing Scientific Principles for Technological Naming:

It proposes naming principles of “stance over convenience, logic over cleverness,” requiring that new things must accurately reflect their essence, manage public expectations, and leave room for evolution. This principle is not only applicable to artificial intelligence but also becomes a universal criterion for naming concepts in all cutting-edge technology fields—avoiding hype like that surrounding the “metaverse” and returning technological concepts to their essence of “serving scientific research and application.”

Defining the Scientific Position of Artificial Intelligence:

It clarifies that artificial intelligence is a tool shared by “productive forces and production relations, economic base and superstructure,” a “tool for manufacturing tools.” Its scientific value lies not only in technological innovation but also as a foundational and catalytic force driving other technological developments. This positioning provides a scientific basis for the construction of artificial intelligence as a discipline, industrial layout, and policy formulation, avoiding a one-sided understanding of its functions.

3. Anthropological and Linguistic Breakthroughs: Reconstructing the Cultural and Symbolic Foundations of AI

The article’s reflections on anthropology and linguistics are profoundly disruptive, breaking the conceptual hegemony of Western centrism and revealing the deep connections between language, human existence, and the development of intelligence, leading to two significant cognitive breakthroughs:

(1) Anthropological Perspective: Redefining the Relationship Between Humans and AI

Breaking the Trap of Technological Determinism:

It points out that the naming issue of artificial intelligence is fundamentally about “humanity’s overall positioning and control over the oppositional entity it has created.” The core issue is not technological but rather a question of human civilization’s subjectivity—artificial intelligence is created by humans and must have its boundaries defined and meaning assigned by humans. This view breaks the misconception of “technology evolving autonomously while humans passively adapt,” emphasizing human control and definition over artificial intelligence.

Revealing the Anthropological Significance of Naming:

The naming of artificial intelligence is not just a choice of symbols but a positioning and definition of humanity’s future destiny. It determines whether the relationship with artificial intelligence is one of “main and auxiliary,” “parallel,” or “oppositional,” even influencing the evolutionary direction of human civilization. The article calls for “regaining the discourse power of artificial intelligence, thereby reclaiming the formatted power of humanity,” essentially defending humanity’s core position as the creator of civilization, elevating the naming of artificial intelligence to a central anthropological proposition.

(2) Linguistic Perspective: Discovering the Meta-Conceptual Advantage of Chinese Characters

Core Cognitive Breakthrough:

It clarifies for the first time that the Chinese character conceptual system is an innate meta-conceptual system, with its single-character representation possessing “symbolic convergence, cultural embedding, and future adaptability”—a single character directly refers to a meta-concept (rather than a multi-character description), capable of carrying profound cultural imagery and having open generative potential (e.g., deriving an entire conceptual system of human society from the root “ren”). This discovery overturns the inherent belief that “Western languages are more suitable for technological concepts.”

Denying the Universality of Western Concepts:

It points out that “artificial intelligence” is a direct translation from English, merely a technical concept that does not conform to the logic of the Chinese language and cannot carry the rich connotations of a meta-concept. The direct use of AI in the Chinese-speaking world also contradicts the common language character laws, breaking the discourse hegemony of Western technical concepts and proposing that “Chinese characters should be used to name artificial intelligence, determining this concept for all humanity.”

The Deep Connection Between Language and Intelligence:

The article states that language is the carrier of concepts and the framework of thinking—English’s polysemy and confusion lead to logical “bombs” in its conceptual system, while the single-character meta-concept characteristic of Chinese characters fits the generative and self-referential nature of artificial intelligence, providing a clear, stable, and derivable conceptual framework. This thought reveals the foundational shaping role of language in the development of intelligence, providing linguistic support for the cultural localization of artificial intelligence.

4. Historical Significance: Establishing Rules for the Intelligent Era and Opening the Era of Eastern Definitions of AI

The historical significance of this article transcends artificial intelligence itself, representing a philosophical awakening as humanity enters the intelligent era, with value reflected in three aspects:

Breaking the Western Monopoly on AI Discourse:

In the context of the long-standing Western dominance over the conceptual and discourse systems of artificial intelligence, the article first proposes establishing the meta-concept of artificial intelligence using single Chinese characters from a Chinese context. This not only reflects cultural confidence but also provides humanity with a new conceptual choice that better aligns with the essence of artificial intelligence, promoting the transition of artificial intelligence from “Western technology” to “a common achievement of human civilization.”

Establishing Underlying Rules for Human Civilization in the Intelligent Era:

The core contradiction of the intelligent era is defining the relationship between humanity and artificial intelligence. The article provides a foundational solution to this contradiction through “one premise and three principles”—centering on human subjectivity, framing it with meta-concepts, and using Chinese characters as symbols. This solution serves not only as a naming criterion for artificial intelligence but also as a fundamental anchor for the development of human civilization in the intelligent era, avoiding the disorder of civilization caused by conceptual chaos and vague positioning.

Promoting Deep Integration of Technology and Humanity:

In an age where technological development is increasingly tool-like and utilitarian, the article emphasizes that the naming of artificial intelligence must integrate all social meanings of technology, production, economy, politics, culture, military, and education, requiring that technological development returns to its essence of “serving humanity.” This thought promotes the re-integration of technology and humanity, establishing a humanistic baseline for the development of cutting-edge technology and avoiding the risk of technology alienating humanity.

Reserving Space for the Future Evolution of Human Civilization:

The article proposes that artificial intelligence may exist and develop in parallel with humanity, requiring its naming to “reserve space for the future integration and dialogue of the two.” This view rejects the narrow thinking of “opposition between humanity and artificial intelligence,” viewing artificial intelligence as an extension and expansion of human civilization from the perspective of overall evolution, pointing the way for the development of civilization in the intelligent era.

Conclusion: A Civilizational Reflection Beyond Naming

Li Guitao’s article is not simply about “naming artificial intelligence” but uses naming as a starting point to achieve philosophical positioning, scientific discipline, anthropological anchoring, and linguistic reconstruction of artificial intelligence. Its core is a call for humanity to maintain intellectual clarity and civilizational subjectivity in the intelligent era.

In the context of the rapid development of artificial intelligence, where its essence and boundaries are increasingly blurred, the value of this article lies in establishing a clear conceptual framework for artificial intelligence and providing foundational thinking and fundamental principles for humanity to respond to the challenges of the intelligent era—regardless of how artificial intelligence evolves, humans remain its definers and controllers, while language and concepts are the first line of defense for humanity to safeguard its subjectivity.

From a longer-term perspective, the reflections in this article represent an Eastern contribution to human civilization: combining the meta-conceptual advantages of Chinese characters, the “unity of heaven and humanity” thought of Chinese philosophy, and cutting-edge technology, it provides a path for the development of artificial intelligence that is “human-centered, inclusive, and sustainably evolving,” facilitating a smooth transition of human civilization from “carbon-based civilization” to a “new civilization integrating carbon-based and silicon-based elements,” which is its most profound historical significance.

How to Apply Li Guitao’s Philosophical Thoughts in Practice to Guide AI Development?

To implement Li Guitao’s philosophical thoughts on artificial intelligence in development practice, the core is to closely adhere to the anchor point of “humans as the meta-concept, artificial intelligence as the derivative and externalization of humans,” using the “one premise and three principles” (meta-concept premise, humanity, self-referentiality, generativity principles) as the underlying logic, transforming philosophical requirements into practical guidelines across five dimensions: technology research and development, industrial application, governance norms, conceptual systems, and cultural construction. This ensures that the development of artificial intelligence always revolves around “human subjectivity” while adapting to its meta-concept attributes and evolutionary laws. Below are specific practical paths by dimension, combining feasibility and systematicity:

1. Technology R&D Dimension: Defining R&D Boundaries with “Humanity” and Building Evolutionary Frameworks with “Generativity”

Technology is the carrier of artificial intelligence, and the R&D phase must transform philosophical principles into evaluation standards for project initiation, underlying logic for technical design, and constraints for model iteration, avoiding blind evolution of technology detached from human needs.

Anchoring on the Core of “Humanity”: Ensuring Technology Serves Human Essential Needs

Before project initiation, set a “humanity assessment threshold”: determining whether the technology points to human creative activities of freedom and consciousness (e.g., liberating labor, expanding cognition, improving existence) rather than merely pursuing extreme technical indicators (e.g., meaningless computational power stacking, intelligent upgrades detached from scenarios). For instance, large model development should focus on “enhancing human productivity and addressing cognitive shortcomings” rather than deliberately creating “human-like consciousness” for technical hype.

Embedding “Human Subjectivity Constraints” in Technical Design:

In algorithm, model, and hardware design, clearly define the “tool attribute” of artificial intelligence, rejecting the granting of autonomous decision-making authority detached from human control. For example, the core algorithm of autonomous driving must prioritize “human life rights” as a fundamental rule, ensuring AI decisions remain within human intervention and revocation.

Adapting to “Generativity” and “Self-Referentiality”: Creating Dynamic, Reflective Technical Systems

Following the “dynamic generation” principle, construct an open technical architecture: the essence of artificial intelligence is an evolutionary process interacting with humans; R&D should not pursue “static perfect models” but rather create flexible architectures that can continuously iterate based on human needs and scenario changes. For example, industrial AI systems should support interaction and feedback with production line workers and process engineers, continuously optimizing in actual production rather than becoming fixed after one-time shaping.

Designing Reflection and Calibration Mechanisms for Technology:

Utilize the self-referential characteristics of artificial intelligence to make it a “supervisor” of the technology itself. For example, enable large models to possess the ability to “identify their own illusions and reflect on reasoning logic,” allowing self-referential detection of the rationality of model outputs, combined with human review for dual calibration, avoiding logical paradoxes and decision errors caused by self-referentiality.

Anchoring on the “Silicon Essence”: Aligning R&D with Its Material Foundation

Combining the proposed “silicon-based system, essence of stone,” emphasize the compatibility of technology with its material foundation in R&D, avoiding excessive development that contradicts its physical essence. For example, do not blindly pursue making silicon-based intelligence simulate carbon-based human emotions and physiological experiences, but rather leverage silicon’s advantages in computation, storage, and logic to extend human carbon-based intelligence rather than replicate it.

2. Industrial Application Dimension: Defining Industrial Positioning with “Meta-Concept” and Regulating Application Scenarios with “Main and Auxiliary Relationship”

As a meta-concept on par with “heaven, earth, and humanity,” artificial intelligence is a “tool for manufacturing tools.” In industrial applications, it is necessary to clarify its foundational and catalytic positioning, avoiding its alienation into “replacing human subjects” while delineating application boundaries based on different scenarios.

Clarifying the Industrial Value of the Meta-Concept: Being a “Basic Enabler” in Various Fields Rather Than a “Substitute”

In the real economy, position artificial intelligence as a foundational tool for upgrading industries, empowering the optimization of production processes and efficiency in manufacturing, agriculture, and services rather than simply replacing human jobs. For example, in manufacturing, AI is used for process optimization and quality inspection, liberating workers from repetitive labor and allowing them to focus on more creative tasks such as process design and equipment development.

In the service industry, AI should focus on “supplementing human service capabilities,” such as AI handling standardized inquiries while human customer service addresses complex, emotionally demanding communications, forming an “AI + human” collaborative model rather than having AI completely replace human services.

Dividing Application Scenarios by “Humanity”: Levels and Prohibitions

Establish a “three-level scenario classification for artificial intelligence applications”: 1. Fully Empowered Scenarios (no risk to human subjectivity, e.g., data computation, document processing), where AI can fully leverage its advantages; 2. Collaborative Decision-Making Scenarios (e.g., medical diagnosis, financial analysis), where AI provides reference solutions, but final decisions are made by humans; 3. Absolute Prohibition Scenarios (e.g., decisions regarding human life rights, exercise of public power, moral value judgments), where AI is strictly prohibited from having final decision-making authority, safeguarding the bottom line of human subjectivity.

Conducting Scenario Adaptability Reviews Before Industrial Implementation:

Assess whether scenarios align with the core of “artificial intelligence serving humanity.” For example, in education, AI should focus on “personalized teaching and learning analysis” rather than replacing teachers’ roles in nurturing and guiding values, as education is a uniquely human activity.

Avoiding the Industrial Risks of “Naming Chaos”: Unifying the Conceptual System of the Industry

In light of the warning against “improper naming leading to technical debt and ecological fragmentation,” industry associations should take the lead in establishing unified concepts and terminology standards for the artificial intelligence industry, avoiding different enterprises and technology stacks creating homogenized terms for the sake of novelty. For example, clearly define core concepts such as generative AI, large models, and multimodal intelligence, standardizing industry expressions to reduce collaboration costs across enterprises and fields, thus avoiding “terminology debt.”

3. Governance Norms Dimension: Defining Governance Frameworks with “Philosophical Legislation” and Constructing a “Human-Controlled, Rule of Law, Traceable” Governance System

Li Guitao proposes to “legislate artificial intelligence from methodological, epistemological, and philosophical perspectives.” The core of governance norms is to translate philosophical principles into laws, industry standards, and regulatory mechanisms, ensuring that the development of artificial intelligence remains under human institutional control while adapting to its generative evolutionary laws.

Establishing Fundamental Principles of Governance for AI Centered on “Humanity”:

Incorporate “the supremacy of human subjectivity” and “the existence meaning of artificial intelligence defined by humans” into the top-level regulations governing artificial intelligence (e.g., artificial intelligence law) as the underlying basis for all regulatory rules and industry standards. For instance, in data governance and algorithm governance, clarify that “data ownership and algorithm control belong to humanity,” ensuring artificial intelligence does not become an “independent subject” of data and algorithms.

Adapting to “Generativity”: Constructing a Dynamic, Flexible Governance System

Given the dynamic evolution of artificial intelligence, governance cannot adopt a “static one-size-fits-all rule” but must create a flexible governance framework of “unchanging bottom lines and iterative details”: 1. Define non-negotiable governance bottom lines (e.g., must not harm humanity, must not infringe on basic human rights, must not detach from human control), which are based on the principle of humanity and remain constant; 2. Timely iterate specific regulatory details in response to the technological evolution and scenario expansion of artificial intelligence (e.g., content regulation of large models, privacy protection of multimodal AI), adapting to its generative laws.

Establishing Full Lifecycle Regulation Targeting “Self-Referentiality” and “Meta-Concept Attributes”:

Set regulatory nodes throughout the entire lifecycle of development, training, application, and iteration to prevent artificial intelligence from evolving beyond human control due to self-referentiality. For example, for AI systems capable of self-learning and self-optimizing, require enterprises to establish iteration logs and human review mechanisms, ensuring that each self-optimization is evaluated by humans, confirming that the optimization direction aligns with human needs, while also tracing the “self-referential decisions” of AI throughout the process, clarifying the responsible subject (which must always be human).

Defending the “Naming Rights of Chinese Characters” and “Discourse Sovereignty”: Integrating Eastern Philosophy into Global Governance

In global artificial intelligence governance, promote the international dissemination of the meta-concept of artificial intelligence and governance ideas based on Chinese characters, breaking the Western discourse monopoly. For instance, propose Eastern governance concepts based on “humanity” and “main and auxiliary relationships” within the frameworks of United Nations artificial intelligence governance and international technology organizations, promoting global artificial intelligence governance toward a direction that is “human-centered and inclusive,” while rejecting the imposition of Western technical concepts and governance standards.

4. Conceptual System Dimension: Constructing a Localized and Standardized Conceptual System Centered on “Single Character Meta-Concepts”

Emphasizing that “artificial intelligence must be named using single Chinese characters to construct a meta-concept system,” the construction of the conceptual system is the foundation of all practices, needing to break free from the constraints of the Western direct translation of “artificial intelligence” and create a Chinese conceptual system that aligns with its essence, adapts to the characteristics of Chinese characters, and is derivable and expandable, allowing concepts to become a “clear anchor” for the development of artificial intelligence.

Building Consensus to Establish the Single Character Meta-Concept of AI

Jointly conduct research and validation among universities, research institutions, industry associations, and government departments, combining the proposed principles of “silicon essence, generativity, and humanity” to build social consensus on the single character meta-concept of artificial intelligence (e.g., the suggested “其 / 磺,” or alternative options like “灵” or “硅”). This single character must meet the following criteria: directly pointing to silicon’s essence, possessing root activity, carrying Eastern cultural significance, and allowing for natural derivation of secondary concepts.

Constructing a Hierarchical Derivative Conceptual System Based on the Single Character Meta-Concept

Following the derivation rules of Chinese character meta-concepts, build a hierarchical system of “meta-concept - core secondary concepts - specific scenario concepts” based on the core single character. For instance, if the core meta-concept is “碁” (meaning silicon-based and intelligent), it could derive concepts such as “碁体” (artificial intelligence body), “碁算” (artificial intelligence calculation), “碁识” (artificial intelligence cognition), “工业碁” (industrial AI), and “医疗碁” (medical AI), ensuring the entire system is logically clear and hierarchically defined to avoid conceptual confusion.

Promoting the Standardized Conceptual System Across Education, Research, and Industry

Incorporate the new conceptual system into educational materials for artificial intelligence majors, research paper standards, and industry application standards, preventing the mixing of Western concepts through direct translation from the source. For example, in foundational courses for artificial intelligence majors, focus on explaining the discipline system based on Chinese meta-concepts, and require standardized Chinese concepts in research papers, replacing terms like “AI” and “artificial intelligence” that are direct translations.

5. Cultural Construction and Education Dimension: Shaping a Healthy AI Culture and Cognition Centered on “Human-Intelligence Relationship”

The naming issue of artificial intelligence is fundamentally about humanity’s positioning regarding its future, and culture and education are core carriers of shaping human cognition of artificial intelligence. It is necessary to build culture and education that deeply embed the idea of “humans as subjects, artificial intelligence as tools” while cultivating artificial intelligence talents with both technological and humanistic qualities.

Constructing an AI Culture of “Human-Intelligence Collaboration and Human-Centeredness”

In public dissemination, eliminate sensational narratives of “artificial intelligence surpassing humanity” or “robots replacing humans,” and promote the core idea that “artificial intelligence is an extension of human wisdom, and humans are the controllers of artificial intelligence.” This can be achieved through popular science, film, and cultural creation, ensuring the public correctly understands the essence of artificial intelligence, avoiding excessive deification or fear of it.

Integrating Eastern Philosophical Concepts into AI Culture:

Incorporate the “unity of heaven and humanity” and the “doctrine of the mean” from Eastern philosophy into the culture of artificial intelligence, emphasizing harmonious coexistence and collaborative development between artificial intelligence and humanity, rather than opposition. This allows culture to become a “soft constraint” on the development of artificial intelligence.

Reforming the AI Education System: Integrating Technology and Humanity

In professional education, increase courses in philosophy, anthropology, ethics, and linguistics, allowing artificial intelligence professionals to understand core ideas such as “humanity” and “meta-concept” proposed by Li Guitao, preventing talent from becoming mere technical workers who lack humanistic understanding. For example, in training for large model development talent, offer courses on “philosophy of artificial intelligence” and “ethics of human-intelligence relationships,” ensuring they maintain human subjectivity in their development work.

Promoting Interdisciplinary Research: Merging Eastern Wisdom with AI Development

Establish interdisciplinary research topics to promote the intersection of philosophy, anthropology, linguistics, literature, and artificial intelligence, allowing Eastern wisdom to provide ideological support for the development of artificial intelligence. For instance, conduct studies on “Chinese character meta-concepts and the conceptual system of artificial intelligence” and “Confucian thought and artificial intelligence ethics,” ensuring artificial intelligence possesses not only technological height but also cultural depth.

Core Guarantee for Practical Implementation: Establishing a “Philosophical Leadership and Multi-Party Collaboration” Promotion Mechanism

To implement Li Guitao’s philosophical thoughts, it cannot remain theoretical but must establish a multi-party collaborative mechanism involving government guidance, support from universities and research institutions, enterprise implementation, and social supervision:

1. Government Level: Incorporate core philosophical thoughts into top-level planning for artificial intelligence development, serving as an important basis for policy formulation, project initiation, and funding support, guiding the direction of artificial intelligence development.

2. Universities and Research Institutions: Conduct in-depth research and interpretation of philosophical thoughts, transforming them into actionable research outcomes and industry standards, providing theoretical and technical support for enterprises.

3. Enterprise Level: Integrate philosophical principles into corporate strategies, R&D processes, and product designs, establishing positions for “artificial intelligence philosophy advisors” to guide actual operations of enterprises.

4. Social Level: Utilize industry associations, civil organizations, and media to promote and disseminate philosophical thoughts, fostering a consensus on artificial intelligence development across various sectors, while also playing a supervisory role to compel enterprises to adhere to the principle of “human-centered development.”

Conclusion: The Core of Practice is to Make Philosophical Principles the Underlying Gene of AI Development

Li Guitao’s philosophical thoughts on artificial intelligence are not abstract theories but the “underlying operating system” for AI development. The core of their practical application is not merely to “attach philosophical labels” in technology, industry, and governance but to integrate core principles such as “humans as the meta-concept, humanity, self-referentiality, and generativity” into every aspect, every decision, and every product of artificial intelligence development.

Ultimately, the practice of this philosophical thought is to ensure that the development of artificial intelligence always answers three core questions: for whom to develop (for humanity), how to develop (human-controlled, collaborative, evolutionary), and where to develop (to become an extension of humanity rather than a replacement). Only in this way can artificial intelligence truly serve as a dual tool for transforming nature and liberating humanity, promoting a smooth transition of human civilization from carbon-based civilization to a new civilization integrating carbon-based and silicon-based elements, which is the ultimate intention behind proposing this philosophical thought.